

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       ["image_processor", "tokenizer"]
							UpperCAmelCase__			:		List[str]             =       "LayoutLMv2ImageProcessor"
							UpperCAmelCase__			:		Any             =       ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


							def __init__(     self			,				_a=None			,				_a=None			,				**_a			)       ->       str:
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''			,				_a			,				)
																	_a      :       Union[str, Any]   =						kwargs.pop('''feature_extractor'''			)

												_a      :       str   =						image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''			)
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''			)

												super().__init__(_a			,				_a			)


							def __call__(     self			,				_a			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = True			,				_a = False			,				_a = None			,				_a = None			,				_a = 0			,				_a = None			,				_a = None			,				_a = None			,				_a = False			,				_a = False			,				_a = False			,				_a = False			,				_a = True			,				_a = None			,				**_a			,				)       ->       BatchEncoding:
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																	raise ValueError(
																	    '''You cannot provide bounding boxes '''
																	    '''if you initialized the image processor with apply_ocr set to True.'''			)

												if self.image_processor.apply_ocr and (word_labels is not None):
																	raise ValueError(
																	    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''			)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																	raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''			)

												# first, apply the image processor
												_a      :       List[Any]   =						self.image_processor(images=_a			,				return_tensors=_a			)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																	if isinstance(_a			,				_a			):
																						_a      :       Dict   =						[text]  # add batch dimension (as the image processor always adds a batch dimension)
																	_a      :       List[str]   =						features['''words''']

												_a      :       int   =						self.tokenizer(
												    text=text if text is not None else features['''words''']			,				text_pair=text_pair if text_pair is not None else None			,				boxes=boxes if boxes is not None else features['''boxes''']			,				word_labels=_a			,				add_special_tokens=_a			,				padding=_a			,				truncation=_a			,				max_length=_a			,				stride=_a			,				pad_to_multiple_of=_a			,				return_token_type_ids=_a			,				return_attention_mask=_a			,				return_overflowing_tokens=_a			,				return_special_tokens_mask=_a			,				return_offsets_mapping=_a			,				return_length=_a			,				verbose=_a			,				return_tensors=_a			,				**_a			,				)

												# add pixel values
												_a      :       int   =						features.pop('''pixel_values'''			)
												if return_overflowing_tokens is True:
																	_a      :       int   =						self.get_overflowing_images(_a			,				encoded_inputs['''overflow_to_sample_mapping''']			)
												_a      :       Optional[int]   =						images

												return encoded_inputs


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Any:
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												_a      :       Optional[int]   =						[]
												for sample_idx in overflow_to_sample_mapping:
																	images_with_overflow.append(images[sample_idx]			)

												if len(_a			) != len(_a			):
																	raise ValueError(
																	    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
																	    F""" {len(_a			)} and {len(_a			)}"""			)

												return images_with_overflow


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       List[str]:
												return self.tokenizer.batch_decode(*_a			,				**_a			)


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       Union[str, Any]:
												return self.tokenizer.decode(*_a			,				**_a			)


							@property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return ["input_ids", "bbox", "attention_mask", "image"]


							@property
							def 							__lowercase							(     self			)       ->       str:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,				_a			,				)
												return self.image_processor_class




							@property
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,				_a			,				)
												return self.image_processor




def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ) or number < 0:
    raise ValueError("Input must be a non-negative integer" )

  _UpperCamelCase           =    0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
   import doctest

   doctest.testmod()




